Our team builds computational and experimental tools for engineering the properties of inorganic materials through synthesis design and control of atomic-scale transformations.
Using DFT and Monte Carlo methods to understand how atomic rearrangements underpin materials functionality.
Shedding light on materials synthesis by using high-temperature X-ray diffraction to monitor reactions in real time.
Developing foundation models to accelerate simulations and automate the analysis of experimental data.
Published in Applied Physics Letters, our Perspective argues that machine learning for diffraction analysis becomes more reliable when inference is constrained by computed priors.
We introduce GALAXI, a scalable framework that combines phase-specific classifiers with Rietveld refinement to identify multiphase mixtures from powder X-ray diffraction.
Undergraduate researcher Ethan Jin has been awarded a place in the UCLA Samueli Summer Undergraduate Research Program (SURP).